---
title: "DSI - Homework 6"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    social: menu
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}

library(flexdashboard)
library(tidyverse)
library(janitor)
library(stringr)
library(forcats)
library(viridis)
library(plotly)
library(httr)
library(jsonlite)

```

```{r loading and cleaning, cache = TRUE }

get_all_inspections <- function(url) {
  
  all_inspections = vector("list", length = 0)
  
  loop_index = 1
  chunk_size = 50000
  DO_NEXT = TRUE
  
  while (DO_NEXT) {
    message("Getting data, page ", loop_index)
    
    all_inspections[[loop_index]] = 
      GET(url,
          query = list(`$order` = "zipcode",
                       `$limit` = chunk_size,
                       `$offset` = as.integer((loop_index - 1) * chunk_size)
                       )
          ) %>%
      content("text") %>%
      fromJSON() %>%
      as_tibble()
    
    DO_NEXT = dim(all_inspections[[loop_index]])[1] == chunk_size
    loop_index = loop_index + 1
  }
  
  all_inspections
  
}

url = "https://data.cityofnewyork.us/resource/9w7m-hzhe.json"

nyc_restaurant_inspections <- get_all_inspections(url) %>%
  bind_rows() %>%
  rename(restaurant_id = camis, restaurant_name = dba) %>%
  mutate(cuisine_description = if_else(cuisine_description == "Café/Coffee/Tea", "Cafe/coffee", cuisine_description),
         cuisine_description = if_else(cuisine_description == "Latin (Cuban, Dominican, Puerto Rican, South & Central American)", "Latin", cuisine_description)) %>% 
  mutate(score = as.numeric(score),
         grade_new = case_when(
           score < 14 ~ "A",
           score >= 14 & score < 28 ~ "B",
           score >= 28 ~ "C")) %>% 
  filter(is.na(grade) == FALSE)
```


Column {data-width=350}
-----------------------------------------------------------------------
### Chart A

```{r}
nyc_restaurant_inspections %>% 
  filter(grade_new %in% c("A", "B", "C")) %>% 
  select(cuisine_description, grade_new) %>%
  group_by(cuisine_description) %>%
  count(grade_new) %>%
  spread(key = grade_new, value = n) %>%
  mutate(total = rowSums(cbind(A, B, C), na.rm = TRUE)) %>% 
  filter(total >= 3000) %>% 
  plot_ly(x = ~cuisine_description, y = ~A, type = 'bar', name = 'A') %>%
  add_trace(y = ~B, name = 'B') %>%
  add_trace(y = ~C, name = 'C') %>%
  layout(yaxis = list(title = 'Count'), barmode = 'stack') %>% 
  layout(title = "Count of restaurant grades by cuisine type",margin = list(b = 75), xaxis = list(title = "", tickangle = 45))

```

Column {data-width=650}
-----------------------------------------------------------------------

### Chart B

```{r}
 
  nyc_restaurant_inspections %>%
  filter(boro == "MANHATTAN") %>%
  mutate(zipcode = as.character(zipcode)) %>%
  group_by(zipcode) %>%
  mutate(num_restaurants_man = n()) %>%
  mutate(text_label = stringr::str_c("Zipcode:", zipcode, " Num_restaurants:", num_restaurants_man)) %>%
  plot_ly(x = ~zipcode, y = ~num_restaurants_man,color = ~boro, type = "scatter", mode = "marker", text = ~text_label) %>% 
  layout(title = "Count of restaurants in each zip code in Manhattan", margin = list(b = 75))
```

### Chart C

```{r}

#Plotly
nyc_restaurant_inspections %>% 
  filter(grade_new == "A", boro != "Missing", score > 0 ) %>% 
  drop_na() %>% 
  plot_ly(y = ~score, color = ~boro, type = "box") %>%
  layout(title = "Distribution of scores within A-graded restaurants, by borough")

```